Substantial progress has been made in development of flexible semiparametric Bayesian methods for assessing relationships between environmental and genetic predictors and the distribution of health responses. We developed an approach that allows efficient identification of single nucleotide polymorphisms predictive of a disease phenotype. The proposed method relied on a novel nonparametric Bayes approach, which adaptively allocated SNPs into an unknown number of clusters based on their impact on the phenotype. This adaptive, probabilistic clustering maintained flexibility while reducing dimensionality. We also developed methods for clustering of hospitals in terms of their distribution of patient outcomes. Previous approaches for clustering focus on a certain aspect of the distribution, such as the mean outcome for a typical patient. Our approach instead groups hospitals having identical aspects of all features of the distribution. This is important, since two hospitals can have the same mean, while having very different patient outcomes among the sickest and healthiest patients. This article was selected at the best paper to be published in the theory and methods section of the Journal of the American Statistical Association next year, and will be highlighted in a special session at the annual Joint Statistical Meeting. We have also developed flexible modeling frameworks that allow distributions of health outcomes to vary nonparametrically with predictors. This allows one to assess differential effects on the most sensitive individuals in a population instead of assuming a treatment effect or adverse effect of an environmental exposure is identical for all individuals. We have used related ideas to assess the impact of weight gain trajectories during pregnancy on maternal and childhood outcomes, such as obesity.